MiniMax M2.5 GGUF underperforms across benchmarks, analysts report

MiniMax M2.5’s GGUF quantizations have stumbled in real‑world testing, according to a series of benchmark posts by independent researcher Benjamin Marie. Marie, who posted his findings on X (formerly Twitter), recorded that the Q4‑through‑Q1 GGUF variants “perform poorly overall” and “none of them come close to the original model” 【source】. His charts show a steep drop in accuracy and coherence compared with the baseline MiniMax M2.5, a pattern that diverges sharply from his Qwen 3.5 GGUF results, where even the TQ1_0 variant “held up well enough” 【source】. The discrepancy underscores a broader lesson: quantization algorithms that preserve performance for one model do not automatically do so for another, and the oft‑cited shortcut “just take Q4, it’ll be fine” fails to generalize 【source】.

The testing process itself was a marathon. Marie disclosed that each GGUF run consumed between ten and twenty hours on an Nvidia H200 accelerator, and the models frequently produced nonsensical output until they hit the maximum sequence length 【source】. He spent over a week aggregating the data, noting that the sluggish inference and garbled text “tended to generate gibberish” 【source】. These practical pain points highlight the hidden cost of aggressive quantization: while model size shrinks, the computational overhead of debugging and re‑running can erode any latency gains, especially for enterprises that need reliable, turn‑key deployments.

The findings arrive at a moment when MiniMax M2—its predecessor—has been lauded as “the new king of open‑source LLMs,” particularly for agentic tool calling, according to VentureBeat’s Carl Franzen 【source】. Franzen’s piece positions MiniMax M2 as a challenger to DeepSeek and Qwen, emphasizing its utility in enterprise workflows that require models to invoke external software. However, the same coverage does not address the quantized M2.5 variants, leaving a gap between the hype surrounding the open‑source lineage and the reality of its compressed forms. The contrast suggests that while the unquantized MiniMax M2 may excel in tool use, its downstream GGUF versions could undermine the very enterprise scenarios they aim to serve.

Analysts observing the open‑source LLM landscape note that robustness across quantization levels is becoming a differentiator. Marie’s data points to a fragility that could deter adopters who rely on low‑precision models for edge deployment. If a model’s performance degrades dramatically after quantization, the promised cost savings and hardware flexibility evaporate, forcing teams to either revert to larger, more expensive models or invest in custom quantization pipelines—both of which dilute the open‑source value proposition. The broader implication is that quantization robustness may soon factor into the competitive rankings of open‑source LLMs, alongside raw benchmark scores and tool‑calling capabilities.

In short, MiniMax M2.5’s GGUF releases have not lived up to expectations, delivering “dramatically short” performance relative to the original, as documented by Marie’s exhaustive testing 【source】. The episode serves as a cautionary tale for the community: quantization is not a one‑size‑fits‑all solution, and the allure of smaller footprints must be balanced against the risk of degraded output. Stakeholders—from developers to enterprise buyers—should scrutinize quantized benchmarks before committing to deployment, lest they inherit the same “gibberish‑until‑max‑length” behavior that plagued Marie’s week‑long experiment.